Traditional collision detection and warning is complicated and difficult to achieve with high accuracy. Usually the sensors (primarily radar and vision) need to detect objects, classify them (for vision based sensors) and then provide warning. Usually detection and classification (labeling of objects) can be erroneous in the presence of occlusion and bad weather.
If cars could warn their drivers of an imminent crash without the need for accurately detecting and classifying objects every time that would improve the system and help reduce accidents. One artificial intelligence based approach is that the system learns based on circumstances and previous experience and provides warning without the need for detection and classification each time. One option for building such a warning system is to ask an expert to describe as many dangerous situations as possible and formalize that information in an automated reasoner that reacts to sensors on the car. However, the circumstances leading to a crash are frequently subtle and may vary for different drivers. Moreover, it may not be possible to predict a crash from a static snapshot of the road. The recent history of the car and other objects on the road may have to be taken into account, as well. It is difficult to know how long such a history should be or what it should be tracking. Yet if the car could learn on its own what to track and how long to keep salient events in memory, these challenges could be overcome. In addition, cars could be trained with different drivers under different circumstances, creating more flexible warning systems.
There is therefore a need in the art for a crash detection network to enable a vehicle to learn to predict crashes from visual input.